Reservoir Characteristics Interpretation by Using Down-Hole Specific Energy With Down-Hole Torque and Drag

Laosripaiboon, Lertsak; Saiwan, Chintana; Prurapark, Ruktai

doi:10.4043/25890-ms

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…Some of the qualitative evaluations from mud logging include ROP interpretation (such as identification of drilling break and drill-off trend), drilling force, and bit evaluation, and specific energy deflection (Provost (1987), Ziaja and Roegiers (1998), Laosripaiboon et al (2015) ). Meanwhile, qualitative evaluations from logging measurement include visualizations of multiple logs , photoelectric (P e ) factor interpretation, and gamma-ray evaluation for shale identification (Gardner and Dumanoir (1980), Serra et al (1985), Dewan (1986) ).…”

Section: Introductionmentioning

confidence: 99%

Automatic lithology prediction from well logging using kernel density estimation

Corina

Hovda

2018

Journal of Petroleum Science and Engineering

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PrefaceThis thesis is a one of my fruitful result of a keen work for the past one semester. This thesis is also part of the requirement for a master degree in Petroleum Engineering, Department of Petroleum Technology and Applied Geophysics, Norwegian University of Science and Technology (NTNU). The study described herein began in spring 2016 to the extent of 30 educational points. Apart from the efforts of myself, the success of this study depends on the guideline of many others. I take this opportunity to thank these people.First, I express my deep gratitude to my supervisor, Sigve Hovda, for the time and constructive response to the work. His enthusiasm working in this project has preserved the optimistic attitude and made this project possible. I would also like to thank Erik Skogen for the supportive discussion of well logging which I found very valuable for this work. I would also like to thank AGR company for providing access to iQx software, including the access to the data. And, I would also like to thank MATLAB © 2015The MathWorks, Inc.,Natick, Massachusetts, United States, for supporting the computation within the study. Among all of the benefits provided by Institute Petroleum of Technology (IPT) NTNU, my curiosity of interdisciplinary working on this project together with these people have benefited myself more than most. Despite the academic support, I would thank my all of my friends from Petroleum Engineering/Petroleum Geosciences, especially cohort 2014, who became my loyal partners during the long journey of 2 years of the master degree. I would also thank my friends from Indonesia who bring the joy during the rough hour working on my thesis.Most importantly, none of this could have happened without my family. To my mom, the person that I adore the most, I sent my deepest love and thank for being there all the times for me. I would also to thank my dad, sister, and brother who never stop giving me comfort even though we are miles away and I am forever grateful.Trondheim, 2016-06-09 Anisa Noor Corina i AbstractThis thesis presents an automatic real-time analysis of lithology interpretation through a method of statistical analysis: kernel probability density method. The goal of this thesis is to develop a method for interpreting and predicting lithology from the borehole geophysical data in real time. Prior to the development, the data is explored to check the data quality and the requirement of data correction. In addition, from exploratory data analysis, the data characteristics can be observed thus the best-fit classification method can be selected. The study focuses on the univariate analysis of gamma-ray data in classifying shale and non-shale lithology. In addition to univariate analysis, a preliminary study of bivariate analysis is also provided in this thesis. The bivariate analysis combines the gamma-ray and the neutron data.Within the study, the models of probability density are constructed by using kernel estimator. The data source for the models are extracted from 3 wells in the North ...

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Section: Introductionmentioning

confidence: 99%

Automatic lithology prediction from well logging using kernel density estimation

Corina

Hovda

2018

Journal of Petroleum Science and Engineering

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Estimation of geomechanical rock characteristics from specific energy data using combination of wavelet transform with ANFIS-PSO algorithm

Behboud

Ramezanzadeh

Tokhmechi

et al. 2023

J Petrol Explor Prod Technol

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The geomechanical characteristics of a drill formation are uncontrollable factors that are crucial to determining the optimal controllable parameters for a drilling operation. In the present study, data collected in wells drilled in the Marun oilfield of southwestern Iran were used to develop adaptive network-based fuzzy inference system (ANFIS) models of geomechanical parameters. The drilling specific energy (DSE) of the formation was calculated using drilling parameters such as weight-on-bit (WOB), rate of penetration (ROP), rotational speed of drilling string (RPM), torque, bit section area, bit hydraulic factor, and bit hydraulic power. A stationary wavelet transform was subsequently used to decompose the DSE signal to the fourth level. The approximation values and details of each level served as inputs for ANFIS models using particle swarm optimization (PSO) algorithm and genetic algorithm (GA). As model outputs, the Young’s Modulus, uniaxial compressive strength (UCS), cohesion coefficient, Poisson’s ratio, and internal friction angle were compared to the geomechanical parameters obtained from petrophysical logs using laboratory-developed empirical relationships. Both models predicted the Young’s modulus, UCS, and cohesion coefficient with high accuracy, but lacked accuracy in predicting the internal friction angle and Poisson’s ratio. The root mean square error (RMSE) and determination coefficient (R2) were lower for the ANFIS-PSO model than for the ANFIS-GA model, indicating that the ANFIS-PSO model presents higher accuracy and better generalization capability than the ANFIS-GA model. As drilling parameters are readily available, the proposed method can provide valuable information for strategizing a drilling operation in the absence of petrophysical logs.

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Developing an Efficient Drilling System by Coupling Torque Modelling with Mechanical Specific Energy

et al. 2018

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Understanding the formation behavior and the drilling operation is essential to optimize the performance of drilling systems. Several studies were conducted to improve the drilling operation in real time basis utilizing different approaches. Numerous mathematical models (analytical or empirical) were developed to relate the drilling parameters with the rate of penetrations and to predict the drilling efficiency. However, these models are arrived at by ignoring some parameters or employing simplifying assumption(s), which may lead to over or under optimistic drilling performance. The main objective of this research is to investigate the analytical and numerical approaches to calculate the torque and drag in drilling operations and produce a simple and robust model using artificial intelligent techniques. More than 22,000 data point from several wells for depth up to 18,000 ft. was used to develop and validate the model reliability. The full profiles of torque and rate of penetrations was determined, also the required energy for drilling each section has been estimated. The developed model could be utilized to define the optimum range for torque, which leads consequently to generate an efficient drilling system and reduce the drilling cost. In this study, rate of penetration (ROP) was determined using the torque profile and mechanical specific energy (MSE) based on real time and rig-site data. Statistical analysis was conducted to understand the importance of drilling parameters on the variations of torque and rate of penetration. Drilling parameters such as weight on bit (WOB), revolution per minute (RPM), fluid circulation rate (Q), and bit hydraulic horse power (HPb) has been studied. Thereafter, artificial neural network (ANN) model was developed to predict the torque and ROP profiles. The suggested models enable the drilling engineers to optimize the drilling parameters in a real-time manner, by changing the surface and controllable parameters in such a way that maintains the drilling operations within the optimum conditions. This research will assist in improving the operations efficiency through optimizing the drilling parameters. A strong robust model was developed which yields high accuracy results when compared with actual field measurements, average absolute percentage error of less than 6.5% was achieved.

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Reservoir Characteristics Interpretation by Using Down-Hole Specific Energy With Down-Hole Torque and Drag

Cited by 4 publications

References 8 publications

Automatic lithology prediction from well logging using kernel density estimation

Automatic lithology prediction from well logging using kernel density estimation

Estimation of geomechanical rock characteristics from specific energy data using combination of wavelet transform with ANFIS-PSO algorithm

Developing an Efficient Drilling System by Coupling Torque Modelling with Mechanical Specific Energy

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